Sea Water Level Estimation Using Six Different Artificial Neural Networks Training Algorithm
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Date
2019Author
ÇUBUKÇU, Esra Aslı
SANCIOĞLU, Sadrettin
DEMİR, Vahdettin
SEVİMLİ, Mehmet Faik
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Water level estimation is important at various time intervals using the records of past time series in water
resources engineering and management. For instance, sea level affects groundwater tables in low-lying coastal
areas, Also hydrological regimes of some coastal rivers. Therefore, a reliable forecast of sea-level variations is
required in coastal engineering and hydrologic studies. In this study, it has been tried to predict the changes in
sea level by six different artificial neural networks (ANN’s) training algorithms (Quasi-Newton, Conjugate
Gradient, Levenberg-Marquardt, One Step Secant, Resilient back propagation and scaled conjugate gradient
algorithms) and multiple linear regression (MLR) methods, three time steps, for a set of time intervals
comprising 6 hour, 12 hour, 18 hour, 24 hour, 2 day time intervals using observed sea levels. The measurements
from a single tide gauge at Hillarys Boat Harbor Western Australia. The results of the ANN’s algorithms are
compared models with respect to root mean square error (RMSE), mean absolute error (MAE), and
determination coefficient (R2). The comparison results indicate that the Levenberg-Marquardt is faster and has a
better accuracy than the other training algorithms in modelling sea level. The Levenberg-Marquardt with RMSE
= 0.004 m, MAE = 0.002 m and R2 = 0.999 in test period was found to be superior in modelling sea level than
the other algorithms, respectively.
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